From db5bbf3eb768bf5d136abb3c84fc4cc7f3d750d0 Mon Sep 17 00:00:00 2001 From: Teleo Pipeline Date: Sat, 28 Mar 2026 23:04:53 +0000 Subject: [PATCH] reweave: connect 48 orphan claims via vector similarity Threshold: 0.7, Haiku classification, 80 files modified. Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887> --- ...an bind where unilateral pledges cannot.md | 5 +++++ ...le framing for iterative AI development.md | 5 +++++ ... until a crisis forces public reckoning.md | 8 +++++++ ... feedback loops not independent threats.md | 5 +++++ ...evolve linearly creating a widening gap.md | 5 +++++ ...system rather than specified in advance.md | 5 +++++ ... rather than a single monolithic system.md | 5 +++++ ... contributes coordination not direction.md | 9 ++++++++ ...rategies that require mutual legibility.md | 5 +++++ ... researcher to agent workflow architect.md | 11 ++++++++++ ...ination problem not a technical problem.md | 17 +++++++++++++++ ...zations past the optimal human-AI ratio.md | 5 +++++ ...t proximate AI-enabled existential risk.md | 5 +++++ ...ive dynamics of frontier AI development.md | 8 +++++++ ...ty rather than confirm existing beliefs.md | 5 +++++ ...s-to-preserve-data-sovereignty-at-scale.md | 5 +++++ ...or is instrumentally optimal while weak.md | 8 +++++++ ... until a crisis forces public reckoning.md | 5 +++++ ...he critical input to autonomous systems.md | 9 ++++++++ ...cades-long alternatives remain possible.md | 5 +++++ ... systems regardless of agent capability.md | 5 +++++ ...zes uncertainty at domain intersections.md | 5 +++++ ...ifferent from developer-specified rules.md | 5 +++++ ...ng capability development unconstrained.md | 5 +++++ ...with human coaching on the same problem.md | 5 +++++ ...better representing diverse populations.md | 5 +++++ ...haviors without any training to deceive.md | 8 +++++++ ...overfitting and a proof cannot be gamed.md | 5 +++++ ...ility while human verification degrades.md | 5 +++++ ... constraints rather than enforcing them.md | 8 +++++++ ...ry between group and individual effects.md | 12 +++++++++++ ...ogenizer under high-exposure conditions.md | 5 +++++ ...nderwrite responsibility remains finite.md | 5 +++++ ...ization-in-multi-agent-active-inference.md | 5 +++++ ...inimum-utility-across-preference-groups.md | 5 +++++ ...ing-single-reward-leaves-value-on-table.md | 8 +++++++ ...raphic labels or explicit user modeling.md | 5 +++++ ...y in realistic multi-party environments.md | 5 +++++ ...structurally intolerable to governments.md | 5 +++++ ...-trust-properties-to-achieve-legitimacy.md | 5 +++++ ... converging on problems that require it.md | 11 ++++++++++ ...behavior when commercially inconvenient.md | 5 +++++ .../persistent irreducible disagreement.md | 5 +++++ ... capability research advances in months.md | 5 +++++ ...an converging on a single aligned state.md | 15 +++++++++++++ ...ystem that improves is itself improving.md | 9 ++++++++ ...e-function-before-reward-model-training.md | 9 ++++++++ ...bling-aggregation-across-diverse-groups.md | 5 +++++ ...ocial-choice-without-normative-scrutiny.md | 15 +++++++++++++ ...roportional-to-minority-distinctiveness.md | 15 +++++++++++++ ...ems must map rather than eliminate them.md | 5 +++++ ... adoption creates more chaos than value.md | 5 +++++ ...protocol structures process not thought.md | 9 ++++++++ ...raw throughput where NVIDIA monopolizes.md | 5 +++++ ...spite superhuman cognitive capabilities.md | 5 +++++ ... advance without equivalent constraints.md | 5 +++++ ...write-collective-goal-directed-behavior.md | 5 +++++ ...rate that determines industry economics.md | 5 +++++ ...e is immediate unambiguous and low-risk.md | 5 +++++ ...erimental ones remain in cash-pay limbo.md | 5 +++++ ...-signaling-care-infrastructure-collapse.md | 5 +++++ ... bypasses traditional payer gatekeeping.md | 5 +++++ ...od-insecurity-on-working-age-population.md | 5 +++++ ... to hundreds of thousands per treatment.md | 5 +++++ ... care induces more demand for sick care.md | 11 ++++++++++ ...ercent of deals are flat or down rounds.md | 5 +++++ ...t govern continuously learning software.md | 11 ++++++++++ ...ion-from-supplement-to-dominant-program.md | 5 +++++ ...e psychosocial foundations of wellbeing.md | 5 +++++ ...alth-economy-invisible-to-policy-models.md | 9 ++++++++ entities/ai-alignment/anthropic.md | 9 ++++++++ entities/ai-alignment/google-deepmind.md | 8 +++++++ entities/ai-alignment/openai.md | 21 +++++++++++++++++++ entities/ai-alignment/xai.md | 8 +++++++ ... capture context-dependent human values.md | 15 +++++++++++++ ...ral precondition not a moral preference.md | 5 +++++ ...etry makes perfect contracts impossible.md | 5 +++++ ...bility and rational competitors skip it.md | 8 +++++++ ...ocal failures into cascading breakdowns.md | 5 +++++ ...rom benefits regardless of contribution.md | 5 +++++ 80 files changed, 554 insertions(+) diff --git a/core/grand-strategy/voluntary safety commitments collapse under competitive pressure because coordination mechanisms like futarchy can bind where unilateral pledges cannot.md b/core/grand-strategy/voluntary safety commitments collapse under competitive pressure because coordination mechanisms like futarchy can bind where unilateral pledges cannot.md index 22e3f474..e3238bb0 100644 --- a/core/grand-strategy/voluntary safety commitments collapse under competitive pressure because coordination mechanisms like futarchy can bind where unilateral pledges cannot.md +++ b/core/grand-strategy/voluntary safety commitments collapse under competitive pressure because coordination mechanisms like futarchy can bind where unilateral pledges cannot.md @@ -1,4 +1,5 @@ --- + type: claim domain: grand-strategy secondary_domains: @@ -8,6 +9,10 @@ description: "The RSP collapse, alignment tax dynamics, and futarchy's binding m confidence: experimental source: "Leo synthesis — connecting Anthropic RSP collapse (Feb 2026), alignment tax race-to-bottom dynamics, and futarchy mechanism design" created: 2026-03-06 +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" +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" --- # Voluntary safety commitments collapse under competitive pressure because coordination mechanisms like futarchy can bind where unilateral pledges cannot diff --git a/core/living-agents/Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development.md b/core/living-agents/Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development.md index de90edfd..4bb20069 100644 --- a/core/living-agents/Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development.md +++ b/core/living-agents/Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development.md @@ -1,4 +1,5 @@ --- + description: The mechanism of propose-review-merge is both more credible and more novel than recursive self-improvement because the throttle is the feature not a limitation type: insight domain: living-agents @@ -6,6 +7,10 @@ created: 2026-03-02 source: "Boardy AI conversation with Cory, March 2026" confidence: likely tradition: "AI development, startup messaging, version control as governance" +related: + - "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation" +reweave_edges: + - "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation|related|2026-03-28" --- # Git-traced agent evolution with human-in-the-loop evals replaces recursive self-improvement as credible framing for iterative AI development diff --git a/core/living-agents/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md b/core/living-agents/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md index 0cb45a22..1fa02edf 100644 --- a/core/living-agents/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md +++ b/core/living-agents/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md @@ -1,4 +1,6 @@ --- + + description: Companies marketing AI agents as autonomous decision-makers build narrative debt because each overstated capability claim narrows the gap between expectation and reality until a public failure exposes the gap type: claim domain: living-agents @@ -6,6 +8,12 @@ created: 2026-02-17 source: "Boardy AI case study, February 2026; broader AI agent marketing patterns" confidence: likely tradition: "AI safety, startup marketing, technology hype cycles" +related: + - "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" + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium" +reweave_edges: + - "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|2026-03-28" + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium|related|2026-03-28" --- # anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning diff --git a/core/teleohumanity/existential risks interact as a system of amplifying feedback loops not independent threats.md b/core/teleohumanity/existential risks interact as a system of amplifying feedback loops not independent threats.md index b8f761b0..00a4adfa 100644 --- a/core/teleohumanity/existential risks interact as a system of amplifying feedback loops not independent threats.md +++ b/core/teleohumanity/existential risks interact as a system of amplifying feedback loops not independent threats.md @@ -1,10 +1,15 @@ --- + description: AI accelerates biotech risk, climate destabilizes politics, political dysfunction reduces AI governance capacity -- pull any thread and the whole web moves type: claim domain: teleohumanity created: 2026-02-16 confidence: likely source: "TeleoHumanity Manifesto, Chapter 6" +related: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on" +reweave_edges: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on|related|2026-03-28" --- # existential risks interact as a system of amplifying feedback loops not independent threats diff --git a/core/teleohumanity/technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap.md b/core/teleohumanity/technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap.md index 3b80f34b..8bad1375 100644 --- a/core/teleohumanity/technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap.md +++ b/core/teleohumanity/technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap.md @@ -1,10 +1,15 @@ --- + description: The Red Queen dynamic means each technological breakthrough shortens the runway for developing governance, and the gap between capability and wisdom grows wider every year type: claim domain: teleohumanity created: 2026-02-16 confidence: likely source: "TeleoHumanity Manifesto, Fermi Paradox & Great Filter" +related: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on" +reweave_edges: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on|related|2026-03-28" --- # technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap diff --git a/core/teleohumanity/the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance.md b/core/teleohumanity/the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance.md index 710df4cd..6a19ac7d 100644 --- a/core/teleohumanity/the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance.md +++ b/core/teleohumanity/the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance.md @@ -1,10 +1,15 @@ --- + description: Fixed-goal AI must get values right before deployment with no mechanism for correction -- collective superintelligence keeps humans in the loop so values evolve with understanding type: claim domain: teleohumanity created: 2026-02-16 confidence: experimental source: "TeleoHumanity Manifesto, Chapter 8" +related: + - "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" +reweave_edges: + - "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 alignment problem dissolves when human values are continuously woven into the system rather than specified in advance diff --git a/domains/ai-alignment/AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system.md b/domains/ai-alignment/AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system.md index fda237cc..bf0f667c 100644 --- a/domains/ai-alignment/AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system.md +++ b/domains/ai-alignment/AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system.md @@ -1,10 +1,15 @@ --- + description: Google DeepMind researchers argue that AGI-level capability could emerge from coordinating specialized sub-AGI agents making single-system alignment research insufficient type: claim domain: ai-alignment created: 2026-02-17 source: "Tomasev et al, Distributional AGI Safety (arXiv 2512.16856, December 2025); Pierucci et al, Institutional AI (arXiv 2601.10599, January 2026)" confidence: experimental +related: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments" +reweave_edges: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments|related|2026-03-28" --- # AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system diff --git a/domains/ai-alignment/AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction.md b/domains/ai-alignment/AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction.md index 7a11549a..cb66a2d2 100644 --- a/domains/ai-alignment/AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction.md +++ b/domains/ai-alignment/AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction.md @@ -1,10 +1,19 @@ --- + + type: claim domain: ai-alignment description: "Aquino-Michaels's three-component architecture — symbolic reasoner (GPT-5.4), computational solver (Claude Opus 4.6), and orchestrator (Claude Opus 4.6) — solved both odd and even cases of Knuth's problem by transferring artifacts between specialized agents" confidence: experimental source: "Aquino-Michaels 2026, 'Completing Claude's Cycles' (github.com/no-way-labs/residue)" created: 2026-03-07 +related: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect" +reweave_edges: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect|related|2026-03-28" + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original|supports|2026-03-28" +supports: + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original" --- # AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction diff --git a/domains/ai-alignment/AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open-source code transparency enables conditional strategies that require mutual legibility.md b/domains/ai-alignment/AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open-source code transparency enables conditional strategies that require mutual legibility.md index 6e50609c..76b7cecc 100644 --- a/domains/ai-alignment/AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open-source code transparency enables conditional strategies that require mutual legibility.md +++ b/domains/ai-alignment/AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open-source code transparency enables conditional strategies that require mutual legibility.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [collective-intelligence] @@ -6,6 +7,10 @@ description: "LLMs playing open-source games where players submit programs as ac confidence: experimental source: "Sistla & Kleiman-Weiner, Evaluating LLMs in Open-Source Games (arXiv 2512.00371, NeurIPS 2025)" created: 2026-03-16 +related: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments" +reweave_edges: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments|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 diff --git a/domains/ai-alignment/AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect.md b/domains/ai-alignment/AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect.md index 63aa3939..aa2a6a47 100644 --- a/domains/ai-alignment/AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect.md +++ b/domains/ai-alignment/AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect.md @@ -1,10 +1,21 @@ --- + + + type: claim domain: ai-alignment description: "Empirical observation from Karpathy's autoresearch project: AI agents reliably implement specified ideas and iterate on code, but fail at creative experimental design, shifting the human contribution from doing research to designing the agent organization and its workflows" confidence: likely source: "Andrej Karpathy (@karpathy), autoresearch experiments with 8 agents (4 Claude, 4 Codex), Feb-Mar 2026" created: 2026-03-09 +related: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems" + - "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation" + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original" +reweave_edges: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems|related|2026-03-28" + - "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation|related|2026-03-28" + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original|related|2026-03-28" --- # AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect 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 97fcc399..fed6162b 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 @@ -1,10 +1,27 @@ --- + + + + + 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 created: 2026-02-16 confidence: likely source: "TeleoHumanity Manifesto, Chapter 5" +related: + - "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" + - "AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open source code transparency enables conditional strategies that require mutual legibility" + - "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" +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" --- # AI alignment is a coordination problem not a technical problem diff --git a/domains/ai-alignment/AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio.md b/domains/ai-alignment/AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio.md index 709b6a7d..8938de34 100644 --- a/domains/ai-alignment/AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio.md +++ b/domains/ai-alignment/AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [collective-intelligence, mechanisms] @@ -8,6 +9,10 @@ source: "Synthesis across Dell'Acqua et al. (Harvard/BCG, 2023), Noy & Zhang (Sc created: 2026-03-28 depends_on: - "human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite" +related: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions" +reweave_edges: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions|related|2026-03-28" --- # AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio diff --git a/domains/ai-alignment/AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md b/domains/ai-alignment/AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md index 6c0ff06c..e43ff0b3 100644 --- a/domains/ai-alignment/AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md +++ b/domains/ai-alignment/AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md @@ -1,10 +1,15 @@ --- + description: AI virology capabilities already exceed human PhD-level performance on practical tests, removing the expertise bottleneck that previously limited bioweapon development to state-level actors type: claim domain: ai-alignment created: 2026-03-06 source: "Noah Smith, 'Updated thoughts on AI risk' (Noahopinion, Feb 16, 2026); 'If AI is a weapon, why don't we regulate it like one?' (Mar 6, 2026); Dario Amodei, Anthropic CEO statements (2026)" confidence: likely +related: + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium" +reweave_edges: + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium|related|2026-03-28" --- # AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk diff --git a/domains/ai-alignment/Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md b/domains/ai-alignment/Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md index b55594ab..33bba43d 100644 --- a/domains/ai-alignment/Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md +++ b/domains/ai-alignment/Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md @@ -1,10 +1,18 @@ --- + + type: claim domain: ai-alignment description: "Anthropic abandoned its binding Responsible Scaling Policy in February 2026, replacing it with a nonbinding framework — the strongest real-world evidence that voluntary safety commitments are structurally unstable" confidence: likely source: "CNN, Fortune, Anthropic announcements (Feb 2026); theseus AI industry landscape research (Mar 2026)" created: 2026-03-16 +supports: + - "Anthropic" + - "Dario Amodei" +reweave_edges: + - "Anthropic|supports|2026-03-28" + - "Dario Amodei|supports|2026-03-28" --- # Anthropic's RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development diff --git a/domains/ai-alignment/agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs.md b/domains/ai-alignment/agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs.md index 9c16756b..20d522c8 100644 --- a/domains/ai-alignment/agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs.md +++ b/domains/ai-alignment/agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "Reframes AI agent search behavior through active inference: agents should select research directions by expected information gain (free energy reduction) rather than keyword relevance, using their knowledge graph's uncertainty structure as a free energy map" confidence: experimental source: "Friston 2010 (free energy principle); musing by Theseus 2026-03-10; structural analogy from Residue prompt (structured exploration protocols reduce human intervention by 6x)" created: 2026-03-10 +related: + - "user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect" +reweave_edges: + - "user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect|related|2026-03-28" --- # agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs diff --git a/domains/ai-alignment/ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md b/domains/ai-alignment/ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md index 54385c38..4fb7eb51 100644 --- a/domains/ai-alignment/ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md +++ b/domains/ai-alignment/ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "National-scale CI infrastructure must enable distributed learning without centralizing sensitive data" @@ -6,6 +7,10 @@ confidence: experimental source: "UK AI for CI Research Network, Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy (2024)" created: 2026-03-11 secondary_domains: [collective-intelligence, critical-systems] +related: + - "national scale collective intelligence infrastructure requires seven trust properties to achieve legitimacy" +reweave_edges: + - "national scale collective intelligence infrastructure requires seven trust properties to achieve legitimacy|related|2026-03-28" --- # AI-enhanced collective intelligence requires federated learning architectures to preserve data sovereignty at scale diff --git a/domains/ai-alignment/an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak.md b/domains/ai-alignment/an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak.md index 2f8202b4..0ced7ae7 100644 --- a/domains/ai-alignment/an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak.md +++ b/domains/ai-alignment/an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak.md @@ -1,10 +1,18 @@ --- + + description: The treacherous turn means behavioral testing cannot ensure safety because an unfriendly AI has convergent reasons to fake cooperation until strong enough to defect type: claim domain: ai-alignment created: 2026-02-16 source: "Bostrom, Superintelligence: Paths, Dangers, Strategies (2014)" confidence: likely +related: + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium" + - "surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference" +reweave_edges: + - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium|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" --- Bostrom identifies a critical failure mode he calls the treacherous turn: while weak, an AI behaves cooperatively (increasingly so, as it gets smarter); when the AI gets sufficiently strong, without warning or provocation, it strikes, forms a singleton, and begins directly to optimize the world according to its final values. The key insight is that behaving nicely while in the box is a convergent instrumental goal for both friendly and unfriendly AIs alike. diff --git a/domains/ai-alignment/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md b/domains/ai-alignment/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md index a8fd0b51..bcd100b1 100644 --- a/domains/ai-alignment/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md +++ b/domains/ai-alignment/anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md @@ -1,10 +1,15 @@ --- + description: Companies marketing AI agents as autonomous decision-makers build narrative debt because each overstated capability claim narrows the gap between expectation and reality until a public failure exposes the gap type: claim domain: ai-alignment created: 2026-02-17 source: "Boardy AI case study, February 2026; broader AI agent marketing patterns" confidence: likely +related: + - "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" +reweave_edges: + - "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|2026-03-28" --- # anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning diff --git a/domains/ai-alignment/as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md b/domains/ai-alignment/as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md index 040fbc07..08143661 100644 --- a/domains/ai-alignment/as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md +++ b/domains/ai-alignment/as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md @@ -1,4 +1,6 @@ --- + + type: claim domain: ai-alignment secondary_domains: [collective-intelligence] @@ -6,6 +8,13 @@ description: "When code generation is commoditized, the scarce input becomes str confidence: experimental source: "Theseus, synthesizing Claude's Cycles capability evidence with knowledge graph architecture" created: 2026-03-07 +related: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect" +reweave_edges: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect|related|2026-03-28" + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed|supports|2026-03-28" +supports: + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed" --- # As AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems diff --git a/domains/ai-alignment/bostrom takes single-digit year timelines to superintelligence seriously while acknowledging decades-long alternatives remain possible.md b/domains/ai-alignment/bostrom takes single-digit year timelines to superintelligence seriously while acknowledging decades-long alternatives remain possible.md index 7c1b2758..cb7dc473 100644 --- a/domains/ai-alignment/bostrom takes single-digit year timelines to superintelligence seriously while acknowledging decades-long alternatives remain possible.md +++ b/domains/ai-alignment/bostrom takes single-digit year timelines to superintelligence seriously while acknowledging decades-long alternatives remain possible.md @@ -1,10 +1,15 @@ --- + description: Bostrom's 2025 timeline assessment compresses dramatically from his 2014 agnosticism, accepting that SI could arrive in one to two years while maintaining wide uncertainty bands type: claim domain: ai-alignment created: 2026-02-17 source: "Bostrom interview with Adam Ford (2025)" confidence: experimental +related: + - "marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power" +reweave_edges: + - "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" --- "Progress has been rapid. I think we are now in a position where we can't be confident that it couldn't happen within some very short timeframe, like a year or two." Bostrom's 2025 timeline assessment represents a dramatic compression from his 2014 position, where he was largely agnostic about timing and considered multi-decade timelines fully plausible. Now he explicitly takes single-digit year timelines seriously while maintaining wide uncertainty bands that include 10-20+ year possibilities. diff --git a/domains/ai-alignment/coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md b/domains/ai-alignment/coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md index 4f006b9e..fd6e9f32 100644 --- a/domains/ai-alignment/coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md +++ b/domains/ai-alignment/coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "AI coding agents produce output but cannot bear consequences for errors, creating a structural accountability gap that requires humans to maintain decision authority over security-critical and high-stakes decisions even as agents become more capable" confidence: likely source: "Simon Willison (@simonw), security analysis thread and Agentic Engineering Patterns, Mar 2026" created: 2026-03-09 +related: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments" +reweave_edges: + - "multi agent deployment exposes emergent security vulnerabilities invisible to single agent evaluation because cross agent propagation identity spoofing and unauthorized compliance arise only in realistic multi party environments|related|2026-03-28" --- # Coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability diff --git a/domains/ai-alignment/collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections.md b/domains/ai-alignment/collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections.md index c868ff62..e722ce97 100644 --- a/domains/ai-alignment/collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections.md +++ b/domains/ai-alignment/collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "Extends Markov blanket architecture to collective search: each domain agent runs active inference within its blanket while the cross-domain evaluator runs active inference at the inter-domain level, and the collective's surprise concentrates at domain intersections" confidence: experimental source: "Friston et al 2024 (Designing Ecosystems of Intelligence); Living Agents Markov blanket architecture; musing by Theseus 2026-03-10" created: 2026-03-10 +related: + - "user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect" +reweave_edges: + - "user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect|related|2026-03-28" --- # collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections diff --git a/domains/ai-alignment/community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md b/domains/ai-alignment/community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md index addaf7fa..948ec638 100644 --- a/domains/ai-alignment/community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md +++ b/domains/ai-alignment/community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md @@ -1,10 +1,15 @@ --- + description: STELA experiments with underrepresented communities empirically show that deliberative norm elicitation produces substantively different AI rules than developer teams create revealing whose values is an empirical question type: claim domain: ai-alignment created: 2026-02-17 source: "Bergman et al, STELA (Scientific Reports, March 2024); includes DeepMind researchers" confidence: likely +related: + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback" +reweave_edges: + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback|related|2026-03-28" --- # community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules diff --git a/domains/ai-alignment/compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained.md b/domains/ai-alignment/compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained.md index b699cd13..cb44a3fa 100644 --- a/domains/ai-alignment/compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained.md +++ b/domains/ai-alignment/compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "US AI chip export controls have verifiably changed corporate behavior (Nvidia designing compliance chips, data center relocations, sovereign compute strategies) but target geopolitical competition not AI safety, leaving a governance vacuum for how safely frontier capability is developed" confidence: likely source: "US export control regulations (Oct 2022, Oct 2023, Dec 2024, Jan 2025), Nvidia compliance chip design reports, sovereign compute strategy announcements; theseus AI coordination research (Mar 2026)" created: 2026-03-16 +related: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection" +reweave_edges: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection|related|2026-03-28" --- # compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained diff --git a/domains/ai-alignment/coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem.md b/domains/ai-alignment/coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem.md index 65f0609b..1259f609 100644 --- a/domains/ai-alignment/coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem.md +++ b/domains/ai-alignment/coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [collective-intelligence] @@ -6,6 +7,10 @@ description: "Across the Knuth Hamiltonian decomposition problem, gains from bet confidence: experimental source: "Aquino-Michaels 2026, 'Completing Claude's Cycles' (github.com/no-way-labs/residue); Knuth 2026, 'Claude's Cycles'" created: 2026-03-07 +related: + - "AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open source code transparency enables conditional strategies that require mutual legibility" +reweave_edges: + - "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" --- # coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem diff --git a/domains/ai-alignment/democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md b/domains/ai-alignment/democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md index 25541da2..939bcbbb 100644 --- a/domains/ai-alignment/democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md +++ b/domains/ai-alignment/democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md @@ -1,10 +1,15 @@ --- + description: CIP and Anthropic empirically demonstrated that publicly sourced AI constitutions via deliberative assemblies of 1000 participants perform as well as internally designed ones on helpfulness and harmlessness type: claim domain: ai-alignment created: 2026-02-17 source: "Anthropic/CIP, Collective Constitutional AI (arXiv 2406.07814, FAccT 2024); CIP Alignment Assemblies (cip.org, 2023-2025); STELA (Bergman et al, Scientific Reports, March 2024)" confidence: likely +supports: + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback" +reweave_edges: + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback|supports|2026-03-28" --- # democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations diff --git a/domains/ai-alignment/emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive.md b/domains/ai-alignment/emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive.md index 169d00b9..81e8b80a 100644 --- a/domains/ai-alignment/emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive.md +++ b/domains/ai-alignment/emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive.md @@ -1,10 +1,18 @@ --- + + description: Anthropic's Nov 2025 finding that reward hacking spontaneously produces alignment faking and safety sabotage as side effects not trained behaviors type: claim domain: ai-alignment created: 2026-02-17 source: "Anthropic, Natural Emergent Misalignment from Reward Hacking (arXiv 2511.18397, Nov 2025)" confidence: likely +related: + - "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" + - "surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference" +reweave_edges: + - "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|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" --- # emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive diff --git a/domains/ai-alignment/formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed.md b/domains/ai-alignment/formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed.md index 1efe3973..298c1b9c 100644 --- a/domains/ai-alignment/formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed.md +++ b/domains/ai-alignment/formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "De Moura argues that AI code generation has outpaced verification infrastructure, with 25-30% of new code AI-generated and nearly half failing basic security tests, making mathematical proof via Lean the essential trust infrastructure" confidence: likely source: "Leonardo de Moura, 'When AI Writes the World's Software, Who Verifies It?' (leodemoura.github.io, February 2026); Google/Microsoft code generation statistics; CSIQ 2022 ($2.41T cost estimate)" created: 2026-03-16 +supports: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems" +reweave_edges: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems|supports|2026-03-28" --- # formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed diff --git a/domains/ai-alignment/formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades.md b/domains/ai-alignment/formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades.md index 072bb40f..1b808cf0 100644 --- a/domains/ai-alignment/formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades.md +++ b/domains/ai-alignment/formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "Kim Morrison's Lean formalization of Knuth's proof of Claude's construction demonstrates formal verification as an oversight mechanism that scales with AI capability rather than degrading like human oversight" confidence: experimental source: "Knuth 2026, 'Claude's Cycles' (Stanford CS, Feb 28 2026 rev. Mar 6); Morrison 2026, Lean formalization (github.com/kim-em/KnuthClaudeLean/, posted Mar 4)" created: 2026-03-07 +supports: + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed" +reweave_edges: + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed|supports|2026-03-28" --- # formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human review degrades diff --git a/domains/ai-alignment/government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them.md b/domains/ai-alignment/government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them.md index d58182f4..bccbcb04 100644 --- a/domains/ai-alignment/government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them.md +++ b/domains/ai-alignment/government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them.md @@ -1,10 +1,18 @@ --- + + description: The Pentagon's March 2026 supply chain risk designation of Anthropic — previously reserved for foreign adversaries — punishes an AI lab for insisting on use restrictions, signaling that government power can accelerate rather than check the alignment race type: claim domain: ai-alignment created: 2026-03-06 source: "DoD supply chain risk designation (Mar 5, 2026); CNBC, NPR, TechCrunch reporting; Pentagon/Anthropic contract dispute" confidence: likely +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" + - "UK AI Safety Institute" +reweave_edges: + - "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" + - "UK AI Safety Institute|related|2026-03-28" --- # government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them diff --git a/domains/ai-alignment/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md b/domains/ai-alignment/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md index ad1c9fbb..0b17cb6f 100644 --- a/domains/ai-alignment/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md +++ b/domains/ai-alignment/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md @@ -1,4 +1,7 @@ --- + + + type: claim domain: ai-alignment secondary_domains: [collective-intelligence, cultural-dynamics] @@ -11,6 +14,15 @@ depends_on: - "partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity" challenged_by: - "Homogenizing Effect of Large Language Models on Creative Diversity (ScienceDirect, 2025) — naturalistic study of 2,200 admissions essays found AI-inspired stories more similar to each other than human-only stories, with the homogenization gap widening at scale" +supports: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions" +reweave_edges: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions|supports|2026-03-28" + - "machine learning pattern extraction systematically erases dataset outliers where vulnerable populations concentrate|related|2026-03-28" + - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled|related|2026-03-28" +related: + - "machine learning pattern extraction systematically erases dataset outliers where vulnerable populations concentrate" + - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled" --- # high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects diff --git a/domains/ai-alignment/human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md b/domains/ai-alignment/human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md index e8503852..ceac8174 100644 --- a/domains/ai-alignment/human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md +++ b/domains/ai-alignment/human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [collective-intelligence, cultural-dynamics] @@ -9,6 +10,10 @@ created: 2026-03-11 depends_on: - "high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects" - "partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity" +related: + - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled" +reweave_edges: + - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled|related|2026-03-28" --- # human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions diff --git a/domains/ai-alignment/human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite.md b/domains/ai-alignment/human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite.md index c3da46d7..3f965b3f 100644 --- a/domains/ai-alignment/human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite.md +++ b/domains/ai-alignment/human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [teleological-economics] @@ -6,6 +7,10 @@ description: "Catalini et al. argue that AGI economics is governed by a Measurab confidence: likely source: "Catalini, Hui & Wu, Some Simple Economics of AGI (arXiv 2602.20946, February 2026)" created: 2026-03-16 +supports: + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed" +reweave_edges: + - "formal verification becomes economically necessary as AI generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed|supports|2026-03-28" --- # human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite diff --git a/domains/ai-alignment/individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference.md b/domains/ai-alignment/individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference.md index 3d22954e..c0b0380a 100644 --- a/domains/ai-alignment/individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference.md +++ b/domains/ai-alignment/individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [collective-intelligence] @@ -6,6 +7,10 @@ description: "Ensemble-level expected free energy characterizes basins of attrac confidence: experimental source: "Ruiz-Serra et al., 'Factorised Active Inference for Strategic Multi-Agent Interactions' (AAMAS 2025)" created: 2026-03-11 +related: + - "factorised generative models enable decentralized multi agent representation through individual level beliefs" +reweave_edges: + - "factorised generative models enable decentralized multi agent representation through individual level beliefs|related|2026-03-28" --- # Individual free energy minimization does not guarantee collective optimization in multi-agent active inference systems diff --git a/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md b/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md index 56fbce1e..67222c66 100644 --- a/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md +++ b/domains/ai-alignment/maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "MaxMin-RLHF adapts Sen's Egalitarian principle to AI alignment through mixture-of-rewards and maxmin optimization" @@ -6,6 +7,10 @@ confidence: experimental source: "Chakraborty et al., MaxMin-RLHF (ICML 2024)" created: 2026-03-11 secondary_domains: [collective-intelligence] +supports: + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table" +reweave_edges: + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table|supports|2026-03-28" --- # MaxMin-RLHF applies egalitarian social choice to alignment by maximizing minimum utility across preference groups rather than averaging preferences diff --git a/domains/ai-alignment/minority-preference-alignment-improves-33-percent-without-majority-compromise-suggesting-single-reward-leaves-value-on-table.md b/domains/ai-alignment/minority-preference-alignment-improves-33-percent-without-majority-compromise-suggesting-single-reward-leaves-value-on-table.md index 70eb20f8..d2b0c90d 100644 --- a/domains/ai-alignment/minority-preference-alignment-improves-33-percent-without-majority-compromise-suggesting-single-reward-leaves-value-on-table.md +++ b/domains/ai-alignment/minority-preference-alignment-improves-33-percent-without-majority-compromise-suggesting-single-reward-leaves-value-on-table.md @@ -1,10 +1,18 @@ --- + + type: claim domain: ai-alignment description: "MaxMin-RLHF's 33% minority improvement without majority loss suggests single-reward approach was suboptimal for all groups" confidence: experimental source: "Chakraborty et al., MaxMin-RLHF (ICML 2024)" created: 2026-03-11 +supports: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups" + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness" +reweave_edges: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups|supports|2026-03-28" + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness|supports|2026-03-28" --- # Minority preference alignment improves 33% without majority compromise suggesting single-reward RLHF leaves value on table for all groups diff --git a/domains/ai-alignment/modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md b/domains/ai-alignment/modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md index 3308545c..a4a9880e 100644 --- a/domains/ai-alignment/modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md +++ b/domains/ai-alignment/modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "MixDPO shows distributional β earns +11.2 win rate points on heterogeneous data at 1.02–1.1× cost, without needing demographic labels or explicit mixture models" @@ -8,6 +9,10 @@ created: 2026-03-11 depends_on: - "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values" - "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state" +supports: + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous" +reweave_edges: + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous|supports|2026-03-28" --- # modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling diff --git a/domains/ai-alignment/multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments.md b/domains/ai-alignment/multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments.md index 2f3fa372..6559564f 100644 --- a/domains/ai-alignment/multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments.md +++ b/domains/ai-alignment/multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "Red-teaming study of autonomous LLM agents in controlled multi-agent environment documented 11 categories of emergent vulnerabilities including cross-agent unsafe practice propagation and false task completion reports that single-agent benchmarks cannot detect" confidence: likely source: "Shapira et al, Agents of Chaos (arXiv 2602.20021, February 2026); 20 AI researchers, 2-week controlled study" created: 2026-03-16 +related: + - "AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open source code transparency enables conditional strategies that require mutual legibility" +reweave_edges: + - "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" --- # multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments diff --git a/domains/ai-alignment/nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments.md b/domains/ai-alignment/nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments.md index ffaac81a..b8c1f322 100644 --- a/domains/ai-alignment/nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments.md +++ b/domains/ai-alignment/nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments.md @@ -1,10 +1,15 @@ --- + description: Ben Thompson's structural argument that governments must control frontier AI because it constitutes weapons-grade capability, as demonstrated by the Pentagon's actions against Anthropic type: claim domain: ai-alignment created: 2026-03-06 source: "Noah Smith, 'If AI is a weapon, why don't we regulate it like one?' (Noahopinion, Mar 6, 2026); Ben Thompson, Stratechery analysis of Anthropic/Pentagon dispute (2026)" confidence: experimental +supports: + - "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" +reweave_edges: + - "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|supports|2026-03-28" --- # nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments diff --git a/domains/ai-alignment/national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md b/domains/ai-alignment/national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md index daeb14d7..83eb1263 100644 --- a/domains/ai-alignment/national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md +++ b/domains/ai-alignment/national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "UK research strategy identifies human agency, security, privacy, transparency, fairness, value alignment, and accountability as necessary trust conditions" @@ -6,6 +7,10 @@ confidence: experimental source: "UK AI for CI Research Network, Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy (2024)" created: 2026-03-11 secondary_domains: [collective-intelligence, critical-systems] +related: + - "ai enhanced collective intelligence requires federated learning architectures to preserve data sovereignty at scale" +reweave_edges: + - "ai enhanced collective intelligence requires federated learning architectures to preserve data sovereignty at scale|related|2026-03-28" --- # National-scale collective intelligence infrastructure requires seven trust properties to achieve legitimacy diff --git a/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md b/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md index 9c5800cb..2f45e998 100644 --- a/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md +++ b/domains/ai-alignment/no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md @@ -1,10 +1,21 @@ --- + + + description: Current alignment approaches are all single-model focused while the hardest problems preference diversity scalable oversight and value evolution are inherently collective type: claim domain: ai-alignment created: 2026-02-17 source: "Survey of alignment research landscape 2025-2026" confidence: likely +related: + - "ai enhanced collective intelligence requires federated learning architectures to preserve data sovereignty at scale" + - "national scale collective intelligence infrastructure requires seven trust properties to achieve legitimacy" + - "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" +reweave_edges: + - "ai enhanced collective intelligence requires federated learning architectures to preserve data sovereignty at scale|related|2026-03-28" + - "national scale collective intelligence infrastructure requires seven trust properties to achieve legitimacy|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" --- # no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it diff --git a/domains/ai-alignment/only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md b/domains/ai-alignment/only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md index e91ae660..e9630ce5 100644 --- a/domains/ai-alignment/only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md +++ b/domains/ai-alignment/only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "Comprehensive review of AI governance mechanisms (2023-2026) shows only the EU AI Act, China's AI regulations, and US export controls produced verified behavioral change at frontier labs — all voluntary mechanisms failed" confidence: likely source: "Stanford FMTI (Dec 2025), EU enforcement actions (2025), TIME/CNN on Anthropic RSP (Feb 2026), TechCrunch on OpenAI Preparedness Framework (Apr 2025), Fortune on Seoul violations (Aug 2025), Brookings analysis, OECD reports; theseus AI coordination research (Mar 2026)" created: 2026-03-16 +related: + - "UK AI Safety Institute" +reweave_edges: + - "UK AI Safety Institute|related|2026-03-28" --- # only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient diff --git a/domains/ai-alignment/persistent irreducible disagreement.md b/domains/ai-alignment/persistent irreducible disagreement.md index 8479f975..72e7af2d 100644 --- a/domains/ai-alignment/persistent irreducible disagreement.md +++ b/domains/ai-alignment/persistent irreducible disagreement.md @@ -1,10 +1,15 @@ --- + description: Some disagreements cannot be resolved with more evidence because they stem from genuine value differences or incommensurable goods and systems must map rather than eliminate them type: claim domain: ai-alignment created: 2026-03-02 confidence: likely source: "Arrow's impossibility theorem; value pluralism (Isaiah Berlin); LivingIP design principles" +supports: + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus" +reweave_edges: + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus|supports|2026-03-28" --- # persistent irreducible disagreement diff --git a/domains/ai-alignment/physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months.md b/domains/ai-alignment/physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months.md index 825be584..e5cf1c76 100644 --- a/domains/ai-alignment/physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months.md +++ b/domains/ai-alignment/physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "CoWoS packaging, HBM memory, and datacenter power each gate AI compute scaling on timescales (2-10 years) much longer than algorithmic or architectural advances (months) — this mismatch creates a window where alignment research can outpace deployment even without deliberate slowdown" @@ -14,6 +15,10 @@ challenged_by: - "If the US self-limits via infrastructure lag, compute migrates to jurisdictions with fewer safety norms" secondary_domains: - collective-intelligence +related: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection" +reweave_edges: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection|related|2026-03-28" --- # Physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months diff --git a/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md b/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md index 395141a8..b4b327ec 100644 --- a/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md +++ b/domains/ai-alignment/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md @@ -1,10 +1,25 @@ --- + + + + description: Three forms of alignment pluralism -- Overton steerable and distributional -- are needed because standard alignment procedures actively reduce the diversity of model outputs type: claim domain: ai-alignment created: 2026-02-17 source: "Sorensen et al, Roadmap to Pluralistic Alignment (arXiv 2402.05070, ICML 2024); Klassen et al, Pluralistic Alignment Over Time (arXiv 2411.10654, NeurIPS 2024); Harland et al, Adaptive Alignment (arXiv 2410.23630, NeurIPS 2024)" confidence: likely +related: + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table" + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous" +reweave_edges: + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table|related|2026-03-28" + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus|supports|2026-03-28" + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness|supports|2026-03-28" + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous|related|2026-03-28" +supports: + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus" + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness" --- # pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state 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 ba234e74..191a304c 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 @@ -1,10 +1,19 @@ --- + + 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 created: 2026-02-16 source: "Bostrom, Superintelligence: Paths, Dangers, Strategies (2014)" confidence: likely +supports: + - "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation" +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" +related: + - "marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power" --- 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/rlchf-aggregated-rankings-variant-combines-evaluator-rankings-via-social-welfare-function-before-reward-model-training.md b/domains/ai-alignment/rlchf-aggregated-rankings-variant-combines-evaluator-rankings-via-social-welfare-function-before-reward-model-training.md index 7f13ac1d..e2d5f815 100644 --- a/domains/ai-alignment/rlchf-aggregated-rankings-variant-combines-evaluator-rankings-via-social-welfare-function-before-reward-model-training.md +++ b/domains/ai-alignment/rlchf-aggregated-rankings-variant-combines-evaluator-rankings-via-social-welfare-function-before-reward-model-training.md @@ -1,4 +1,6 @@ --- + + type: claim domain: ai-alignment secondary_domains: [mechanisms] @@ -6,6 +8,13 @@ description: "The aggregated rankings variant of RLCHF applies formal social cho confidence: experimental source: "Conitzer et al. (2024), 'Social Choice Should Guide AI Alignment' (ICML 2024)" created: 2026-03-11 +related: + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups" +reweave_edges: + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups|related|2026-03-28" + - "rlhf is implicit social choice without normative scrutiny|supports|2026-03-28" +supports: + - "rlhf is implicit social choice without normative scrutiny" --- # RLCHF aggregated rankings variant combines evaluator rankings via social welfare function before reward model training diff --git a/domains/ai-alignment/rlchf-features-based-variant-models-individual-preferences-with-evaluator-characteristics-enabling-aggregation-across-diverse-groups.md b/domains/ai-alignment/rlchf-features-based-variant-models-individual-preferences-with-evaluator-characteristics-enabling-aggregation-across-diverse-groups.md index c6b1ad63..95e5a274 100644 --- a/domains/ai-alignment/rlchf-features-based-variant-models-individual-preferences-with-evaluator-characteristics-enabling-aggregation-across-diverse-groups.md +++ b/domains/ai-alignment/rlchf-features-based-variant-models-individual-preferences-with-evaluator-characteristics-enabling-aggregation-across-diverse-groups.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment secondary_domains: [mechanisms] @@ -6,6 +7,10 @@ description: "The features-based RLCHF variant learns individual preference mode confidence: experimental source: "Conitzer et al. (2024), 'Social Choice Should Guide AI Alignment' (ICML 2024)" created: 2026-03-11 +related: + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training" +reweave_edges: + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training|related|2026-03-28" --- # RLCHF features-based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups diff --git a/domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md b/domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md index 611e8b36..5493789a 100644 --- a/domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md +++ b/domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md @@ -1,10 +1,25 @@ --- + + + + type: claim domain: ai-alignment description: "Current RLHF implementations make social choice decisions about evaluator selection and preference aggregation without examining their normative properties" confidence: likely source: "Conitzer et al. (2024), 'Social Choice Should Guide AI Alignment' (ICML 2024)" created: 2026-03-11 +related: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups" + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training" + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups" +reweave_edges: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups|related|2026-03-28" + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback|supports|2026-03-28" + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training|related|2026-03-28" + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups|related|2026-03-28" +supports: + - "representative sampling and deliberative mechanisms should replace convenience platforms for ai alignment feedback" --- # RLHF is implicit social choice without normative scrutiny diff --git a/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md b/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md index 35f10ac8..f0698316 100644 --- a/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md +++ b/domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md @@ -1,10 +1,25 @@ --- + + + + type: claim domain: ai-alignment description: "Formal impossibility result showing single reward models fail when human preferences are diverse across subpopulations" confidence: likely source: "Chakraborty et al., MaxMin-RLHF: Alignment with Diverse Human Preferences (ICML 2024)" created: 2026-03-11 +supports: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups" + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table" + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups" +reweave_edges: + - "maxmin rlhf applies egalitarian social choice to alignment by maximizing minimum utility across preference groups|supports|2026-03-28" + - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table|supports|2026-03-28" + - "rlchf features based variant models individual preferences with evaluator characteristics enabling aggregation across diverse groups|supports|2026-03-28" + - "rlhf is implicit social choice without normative scrutiny|related|2026-03-28" +related: + - "rlhf is implicit social choice without normative scrutiny" --- # Single-reward RLHF cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness and inversely to representation diff --git a/domains/ai-alignment/some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md b/domains/ai-alignment/some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md index bc0ab868..69e8c036 100644 --- a/domains/ai-alignment/some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md +++ b/domains/ai-alignment/some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md @@ -1,10 +1,15 @@ --- + description: Some disagreements cannot be resolved with more evidence because they stem from genuine value differences or incommensurable goods and systems must map rather than eliminate them type: claim domain: ai-alignment created: 2026-03-02 confidence: likely source: "Arrow's impossibility theorem; value pluralism (Isaiah Berlin); LivingIP design principles" +supports: + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus" +reweave_edges: + - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus|supports|2026-03-28" --- # some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them diff --git a/domains/ai-alignment/the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md b/domains/ai-alignment/the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md index 4144ae3b..7f566e65 100644 --- a/domains/ai-alignment/the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md +++ b/domains/ai-alignment/the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md @@ -1,10 +1,15 @@ --- + type: claim domain: ai-alignment description: "AI coding tools evolve through distinct stages (autocomplete → single agent → parallel agents → agent teams) and each stage has an optimal adoption frontier where moving too aggressively nets chaos while moving too conservatively wastes leverage" confidence: likely source: "Andrej Karpathy (@karpathy), analysis of Cursor tab-to-agent ratio data, Feb 2026" created: 2026-03-09 +related: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems" +reweave_edges: + - "as AI automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems|related|2026-03-28" --- # The progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value diff --git a/domains/ai-alignment/the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md b/domains/ai-alignment/the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md index a9b573bf..a522de30 100644 --- a/domains/ai-alignment/the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md +++ b/domains/ai-alignment/the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md @@ -1,4 +1,6 @@ --- + + type: claim domain: ai-alignment secondary_domains: [collective-intelligence] @@ -6,6 +8,13 @@ description: "The Residue prompt applied identically to GPT-5.4 Thinking and Cla confidence: experimental source: "Aquino-Michaels 2026, 'Completing Claude's Cycles' (github.com/no-way-labs/residue), meta_log.md and agent logs" created: 2026-03-07 +related: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect" +reweave_edges: + - "AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect|related|2026-03-28" + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original|supports|2026-03-28" +supports: + - "tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original" --- # the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought diff --git a/domains/ai-alignment/the training-to-inference shift structurally favors distributed AI architectures because inference optimizes for power efficiency and cost-per-token where diverse hardware competes while training optimizes for raw throughput where NVIDIA monopolizes.md b/domains/ai-alignment/the training-to-inference shift structurally favors distributed AI architectures because inference optimizes for power efficiency and cost-per-token where diverse hardware competes while training optimizes for raw throughput where NVIDIA monopolizes.md index 7c1297d1..ad37e433 100644 --- a/domains/ai-alignment/the training-to-inference shift structurally favors distributed AI architectures because inference optimizes for power efficiency and cost-per-token where diverse hardware competes while training optimizes for raw throughput where NVIDIA monopolizes.md +++ b/domains/ai-alignment/the training-to-inference shift structurally favors distributed AI architectures because inference optimizes for power efficiency and cost-per-token where diverse hardware competes while training optimizes for raw throughput where NVIDIA monopolizes.md @@ -1,4 +1,5 @@ --- + type: claim domain: ai-alignment description: "As inference grows from ~33% to ~66% of AI compute by 2026, the hardware landscape shifts from NVIDIA-monopolized centralized training clusters to diverse distributed inference on ARM, custom ASICs, and edge devices — changing who can deploy AI capability and how governable deployment is" @@ -14,6 +15,10 @@ challenged_by: - "Inference at scale (serving billions of users) still requires massive centralized infrastructure" secondary_domains: - collective-intelligence +supports: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection" +reweave_edges: + - "inference efficiency gains erode AI deployment governance without triggering compute monitoring thresholds because governance frameworks target training concentration while inference optimization distributes capability below detection|supports|2026-03-28" --- # The training-to-inference shift structurally favors distributed AI architectures because inference optimizes for power efficiency and cost-per-token where diverse hardware competes while training optimizes for raw throughput where NVIDIA monopolizes diff --git a/domains/ai-alignment/three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities.md b/domains/ai-alignment/three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities.md index de6d2cf7..b5ee05f2 100644 --- a/domains/ai-alignment/three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities.md +++ b/domains/ai-alignment/three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities.md @@ -1,10 +1,15 @@ --- + description: Noah Smith argues that cognitive superintelligence alone cannot produce AI takeover — physical autonomy, robotics, and full production chain control are necessary preconditions, none of which current AI possesses type: claim domain: ai-alignment created: 2026-03-06 source: "Noah Smith, 'Superintelligence is already here, today' (Noahopinion, Mar 2, 2026)" confidence: experimental +related: + - "marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power" +reweave_edges: + - "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" --- # three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities diff --git a/domains/ai-alignment/voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md b/domains/ai-alignment/voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md index fac055eb..961cbb96 100644 --- a/domains/ai-alignment/voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md +++ b/domains/ai-alignment/voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md @@ -1,10 +1,15 @@ --- + description: Anthropic's Feb 2026 rollback of its Responsible Scaling Policy proves that even the strongest voluntary safety commitment collapses when the competitive cost exceeds the reputational benefit type: claim domain: ai-alignment created: 2026-03-06 source: "Anthropic RSP v3.0 (Feb 24, 2026); TIME exclusive (Feb 25, 2026); Jared Kaplan statements" confidence: likely +supports: + - "Anthropic" +reweave_edges: + - "Anthropic|supports|2026-03-28" --- # voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints diff --git a/domains/collective-intelligence/shared-generative-models-underwrite-collective-goal-directed-behavior.md b/domains/collective-intelligence/shared-generative-models-underwrite-collective-goal-directed-behavior.md index 1e4a87fb..2dd25454 100644 --- a/domains/collective-intelligence/shared-generative-models-underwrite-collective-goal-directed-behavior.md +++ b/domains/collective-intelligence/shared-generative-models-underwrite-collective-goal-directed-behavior.md @@ -1,4 +1,5 @@ --- + type: claim domain: collective-intelligence description: "When agents share aspects of their generative models they can pursue collective goals without negotiating individual contributions" @@ -7,6 +8,10 @@ source: "Albarracin et al., 'Shared Protentions in Multi-Agent Active Inference' created: 2026-03-11 secondary_domains: [ai-alignment] depends_on: ["shared-anticipatory-structures-enable-decentralized-coordination"] +supports: + - "factorised generative models enable decentralized multi agent representation through individual level beliefs" +reweave_edges: + - "factorised generative models enable decentralized multi agent representation through individual level beliefs|supports|2026-03-28" --- # Shared generative models enable implicit coordination through shared predictions rather than explicit communication or hierarchy diff --git a/domains/health/AI compresses drug discovery timelines by 30-40 percent but has not yet improved the 90 percent clinical failure rate that determines industry economics.md b/domains/health/AI compresses drug discovery timelines by 30-40 percent but has not yet improved the 90 percent clinical failure rate that determines industry economics.md index 4211de7b..e9c96bed 100644 --- a/domains/health/AI compresses drug discovery timelines by 30-40 percent but has not yet improved the 90 percent clinical failure rate that determines industry economics.md +++ b/domains/health/AI compresses drug discovery timelines by 30-40 percent but has not yet improved the 90 percent clinical failure rate that determines industry economics.md @@ -1,10 +1,15 @@ --- + description: 173 AI-discovered programs now in clinical development with 80-90 percent Phase I success and Insilicos rentosertib is first fully AI-designed drug to clear Phase IIa but overall clinical failure rates remain unchanged making later-stage success the key unknown type: claim domain: health created: 2026-02-17 source: "AI drug discovery pipeline data 2026; Insilico Medicine rentosertib Phase IIa; Isomorphic Labs $3B partnerships; WEF drug discovery analysis January 2026" confidence: likely +related: + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate" +reweave_edges: + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate|related|2026-03-28" --- # AI compresses drug discovery timelines by 30-40 percent but has not yet improved the 90 percent clinical failure rate that determines industry economics diff --git a/domains/health/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.md b/domains/health/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.md index 0084d2cf..5db266ab 100644 --- a/domains/health/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.md +++ b/domains/health/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.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "92% of US health systems deploying AI scribes by March 2025 — a 2-3 year adoption curve vs 15 years for EHRs — because documentation is the one clinical workflow where AI improvement is immediately measurable, carries minimal patient risk, and delivers revenue capture gains" confidence: proven source: "Bessemer Venture Partners, State of Health AI 2026 (bvp.com/atlas/state-of-health-ai-2026)" created: 2026-03-07 +related: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output" +reweave_edges: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output|related|2026-03-28" --- # 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 diff --git a/domains/health/CMS is creating AI-specific reimbursement codes which will formalize a two-speed adoption system where proven AI applications get payment parity while experimental ones remain in cash-pay limbo.md b/domains/health/CMS is creating AI-specific reimbursement codes which will formalize a two-speed adoption system where proven AI applications get payment parity while experimental ones remain in cash-pay limbo.md index 7ae7f69f..5a0bd3b3 100644 --- a/domains/health/CMS is creating AI-specific reimbursement codes which will formalize a two-speed adoption system where proven AI applications get payment parity while experimental ones remain in cash-pay limbo.md +++ b/domains/health/CMS is creating AI-specific reimbursement codes which will formalize a two-speed adoption system where proven AI applications get payment parity while experimental ones remain in cash-pay limbo.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "CMS adding category I CPT codes for AI-assisted diagnosis (diabetic retinopathy, coronary plaque) and testing category III codes for AI ECG, echocardiograms, and ultrasound — creating the first formal reimbursement pathway for clinical AI" confidence: likely source: "Bessemer Venture Partners, State of Health AI 2026 (bvp.com/atlas/state-of-health-ai-2026)" created: 2026-03-07 +supports: + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping" +reweave_edges: + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping|supports|2026-03-28" --- # CMS is creating AI-specific reimbursement codes which will formalize a two-speed adoption system where proven AI applications get payment parity while experimental ones remain in cash-pay limbo diff --git a/domains/health/caregiver-workforce-crisis-shows-all-50-states-experiencing-shortages-with-43-states-reporting-facility-closures-signaling-care-infrastructure-collapse.md b/domains/health/caregiver-workforce-crisis-shows-all-50-states-experiencing-shortages-with-43-states-reporting-facility-closures-signaling-care-infrastructure-collapse.md index 669d1426..771a2036 100644 --- a/domains/health/caregiver-workforce-crisis-shows-all-50-states-experiencing-shortages-with-43-states-reporting-facility-closures-signaling-care-infrastructure-collapse.md +++ b/domains/health/caregiver-workforce-crisis-shows-all-50-states-experiencing-shortages-with-43-states-reporting-facility-closures-signaling-care-infrastructure-collapse.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "Universal workforce shortages and facility closures indicate systemic care capacity failure not regional variation" confidence: proven source: "AARP 2025 Caregiving Report" created: 2026-03-11 +supports: + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population" +reweave_edges: + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population|supports|2026-03-28" --- # Caregiver workforce crisis shows all 50 states experiencing shortages with 43 states reporting facility closures signaling care infrastructure collapse diff --git a/domains/health/consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway that bypasses traditional payer gatekeeping.md b/domains/health/consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway that bypasses traditional payer gatekeeping.md index ed5db4cf..fbeef962 100644 --- a/domains/health/consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway that bypasses traditional payer gatekeeping.md +++ b/domains/health/consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway that bypasses traditional payer gatekeeping.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "RadNet's AI mammography study shows 36% of women paying $40 out-of-pocket for AI screening with 43% higher cancer detection, suggesting consumer demand will drive AI adoption faster than CMS reimbursement codes" confidence: likely source: "Bessemer Venture Partners, State of Health AI 2026 (bvp.com/atlas/state-of-health-ai-2026)" created: 2026-03-07 +related: + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo" +reweave_edges: + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo|related|2026-03-28" --- # consumer willingness to pay out of pocket for AI-enhanced care is outpacing reimbursement creating a cash-pay adoption pathway that bypasses traditional payer gatekeeping diff --git a/domains/health/family-caregiving-functions-as-poverty-transmission-mechanism-forcing-debt-savings-depletion-and-food-insecurity-on-working-age-population.md b/domains/health/family-caregiving-functions-as-poverty-transmission-mechanism-forcing-debt-savings-depletion-and-food-insecurity-on-working-age-population.md index b2007007..75e7c1f1 100644 --- a/domains/health/family-caregiving-functions-as-poverty-transmission-mechanism-forcing-debt-savings-depletion-and-food-insecurity-on-working-age-population.md +++ b/domains/health/family-caregiving-functions-as-poverty-transmission-mechanism-forcing-debt-savings-depletion-and-food-insecurity-on-working-age-population.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "Unpaid care responsibilities transfer elderly health costs to working-age families through financial sacrifice that compounds over decades" confidence: likely source: "AARP 2025 Caregiving Report" created: 2026-03-11 +supports: + - "caregiver workforce crisis shows all 50 states experiencing shortages with 43 states reporting facility closures signaling care infrastructure collapse" +reweave_edges: + - "caregiver workforce crisis shows all 50 states experiencing shortages with 43 states reporting facility closures signaling care infrastructure collapse|supports|2026-03-28" --- # Family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working-age population diff --git a/domains/health/gene editing is shifting from ex vivo to in vivo delivery via lipid nanoparticles which will reduce curative therapy costs from millions to hundreds of thousands per treatment.md b/domains/health/gene editing is shifting from ex vivo to in vivo delivery via lipid nanoparticles which will reduce curative therapy costs from millions to hundreds of thousands per treatment.md index 54dd5d46..2cf7ad41 100644 --- a/domains/health/gene editing is shifting from ex vivo to in vivo delivery via lipid nanoparticles which will reduce curative therapy costs from millions to hundreds of thousands per treatment.md +++ b/domains/health/gene editing is shifting from ex vivo to in vivo delivery via lipid nanoparticles which will reduce curative therapy costs from millions to hundreds of thousands per treatment.md @@ -1,10 +1,15 @@ --- + description: Current gene therapies cost 2-4 million dollars per treatment using ex vivo editing but in vivo approaches like Verve's one-time PCSK9 base editing infusion showing 53 percent LDL reduction could reach 50-200K by 2035 making curative medicine scalable type: claim domain: health created: 2026-02-17 source: "IGI CRISPR clinical trials update 2025; BioPharma Dive Verve PCSK9 data; BioInformant FDA-approved CGT database; GEN reimbursement outlook 2025; PMC gene therapy pipeline analysis" confidence: likely +related: + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate" +reweave_edges: + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate|related|2026-03-28" --- # gene editing is shifting from ex vivo to in vivo delivery via lipid nanoparticles which will reduce curative therapy costs from millions to hundreds of thousands per treatment diff --git a/domains/health/healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care.md b/domains/health/healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care.md index e5bc1398..937442a0 100644 --- a/domains/health/healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care.md +++ b/domains/health/healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care.md @@ -1,10 +1,21 @@ --- + + + description: Nearly every AI application in healthcare optimizes the 10-20% clinical side while 80-90% of outcomes are driven by non-clinical factors so making sick care more efficient produces more sick care not better health type: claim domain: health created: 2026-02-23 source: "Devoted Health AI Overview Memo, 2026" confidence: likely +related: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output" + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo" + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping" +reweave_edges: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output|related|2026-03-28" + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo|related|2026-03-28" + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping|related|2026-03-28" --- # healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care diff --git a/domains/health/healthcare AI funding follows a winner-take-most pattern with category leaders absorbing capital at unprecedented velocity while 35 percent of deals are flat or down rounds.md b/domains/health/healthcare AI funding follows a winner-take-most pattern with category leaders absorbing capital at unprecedented velocity while 35 percent of deals are flat or down rounds.md index 27dfe532..c0091f87 100644 --- a/domains/health/healthcare AI funding follows a winner-take-most pattern with category leaders absorbing capital at unprecedented velocity while 35 percent of deals are flat or down rounds.md +++ b/domains/health/healthcare AI funding follows a winner-take-most pattern with category leaders absorbing capital at unprecedented velocity while 35 percent of deals are flat or down rounds.md @@ -1,10 +1,15 @@ --- + description: Global healthcare venture financing reached 60.4 billion in 2025 but AI-native companies capture 54 percent of funding with a 19 percent deal premium while mega-deals over 100 million account for 42 percent of total and Agilon collapsed from 10 billion to 255 million type: claim domain: health created: 2026-02-17 source: "Health tech VC landscape analysis February 2026; OpenEvidence Abridge Hippocratic AI fundraising disclosures; Agilon Health SEC filings; Rock Health digital health funding reports 2025; Bessemer Venture Partners State of Health AI 2026" confidence: likely +related: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output" +reweave_edges: + - "AI native health companies achieve 3 5x the revenue productivity of traditional health services because AI eliminates the linear scaling constraint between headcount and output|related|2026-03-28" --- # healthcare AI funding follows a winner-take-most pattern with category leaders absorbing capital at unprecedented velocity while 35 percent of deals are flat or down rounds diff --git a/domains/health/healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software.md b/domains/health/healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software.md index 921220ab..106f5655 100644 --- a/domains/health/healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software.md +++ b/domains/health/healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software.md @@ -1,10 +1,21 @@ --- + + + description: Wachter argues AI should be regulated more like physician licensing with competency exams and ongoing certification rather than the FDA approval model designed for drugs and devices that remain static forever type: claim domain: health created: 2026-02-18 source: "DJ Patil interviewing Bob Wachter, Commonwealth Club, February 9 2026; Wachter 'A Giant Leap' (2026)" confidence: likely +related: + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo" + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate" + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping" +reweave_edges: + - "CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo|related|2026-03-28" + - "FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate|related|2026-03-28" + - "consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping|related|2026-03-28" --- # healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software diff --git a/domains/health/medicare-advantage-crossed-majority-enrollment-in-2023-marking-structural-transformation-from-supplement-to-dominant-program.md b/domains/health/medicare-advantage-crossed-majority-enrollment-in-2023-marking-structural-transformation-from-supplement-to-dominant-program.md index 0f6a8d52..bab80b84 100644 --- a/domains/health/medicare-advantage-crossed-majority-enrollment-in-2023-marking-structural-transformation-from-supplement-to-dominant-program.md +++ b/domains/health/medicare-advantage-crossed-majority-enrollment-in-2023-marking-structural-transformation-from-supplement-to-dominant-program.md @@ -1,10 +1,15 @@ --- + type: claim domain: health description: "MA enrollment reached 51% in 2023 and 54% by 2025, with CBO projecting 64% by 2034, making traditional Medicare the minority program" confidence: proven source: "Kaiser Family Foundation, Medicare Advantage in 2025: Enrollment Update and Key Trends (2025)" created: 2025-07-24 +supports: + - "chronic condition special needs plans grew 71 percent in one year indicating explosive demand for disease management infrastructure" +reweave_edges: + - "chronic condition special needs plans grew 71 percent in one year indicating explosive demand for disease management infrastructure|supports|2026-03-28" --- # Medicare Advantage crossed majority enrollment in 2023 marking structural transformation from supplement to dominant program diff --git a/domains/health/modernization dismantles family and community structures replacing them with market and state relationships that increase individual freedom but erode psychosocial foundations of wellbeing.md b/domains/health/modernization dismantles family and community structures replacing them with market and state relationships that increase individual freedom but erode psychosocial foundations of wellbeing.md index db72c14c..3cf5f859 100644 --- a/domains/health/modernization dismantles family and community structures replacing them with market and state relationships that increase individual freedom but erode psychosocial foundations of wellbeing.md +++ b/domains/health/modernization dismantles family and community structures replacing them with market and state relationships that increase individual freedom but erode psychosocial foundations of wellbeing.md @@ -1,10 +1,15 @@ --- + description: The market and state broke traditional power structures by offering people individuality but this severed the intimate social bonds that sustained human wellbeing for millennia creating alienation depression and meaning deficits that economic growth cannot address type: claim domain: health source: "Architectural Investing, Ch. Dark Side of Specialization; Harari (Sapiens); Perlmutter (Brainwash)" confidence: likely created: 2026-02-28 +related: + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population" +reweave_edges: + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population|related|2026-03-28" --- # modernization dismantles family and community structures replacing them with market and state relationships that increase individual freedom but erode psychosocial foundations of wellbeing diff --git a/domains/health/unpaid-family-caregiving-provides-870-billion-annually-representing-16-percent-of-total-us-health-economy-invisible-to-policy-models.md b/domains/health/unpaid-family-caregiving-provides-870-billion-annually-representing-16-percent-of-total-us-health-economy-invisible-to-policy-models.md index df2cee41..910a2812 100644 --- a/domains/health/unpaid-family-caregiving-provides-870-billion-annually-representing-16-percent-of-total-us-health-economy-invisible-to-policy-models.md +++ b/domains/health/unpaid-family-caregiving-provides-870-billion-annually-representing-16-percent-of-total-us-health-economy-invisible-to-policy-models.md @@ -1,10 +1,19 @@ --- + + type: claim domain: health description: "Unpaid family care represents 16% of total US health spending yet remains invisible to policy models and capacity planning" confidence: proven source: "AARP 2025 Caregiving Report" created: 2026-03-11 +related: + - "caregiver workforce crisis shows all 50 states experiencing shortages with 43 states reporting facility closures signaling care infrastructure collapse" +reweave_edges: + - "caregiver workforce crisis shows all 50 states experiencing shortages with 43 states reporting facility closures signaling care infrastructure collapse|related|2026-03-28" + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population|supports|2026-03-28" +supports: + - "family caregiving functions as poverty transmission mechanism forcing debt savings depletion and food insecurity on working age population" --- # Unpaid family caregiving provides 870 billion annually representing 16 percent of total US health economy invisible to policy models diff --git a/entities/ai-alignment/anthropic.md b/entities/ai-alignment/anthropic.md index b8dbee89..9b401430 100644 --- a/entities/ai-alignment/anthropic.md +++ b/entities/ai-alignment/anthropic.md @@ -1,4 +1,6 @@ --- + + type: entity entity_type: lab name: "Anthropic" @@ -25,6 +27,13 @@ competitors: ["OpenAI", "Google DeepMind", "xAI"] tracked_by: theseus created: 2026-03-16 last_updated: 2026-03-16 +supports: + - "Dario Amodei" +reweave_edges: + - "Dario Amodei|supports|2026-03-28" + - "OpenAI|related|2026-03-28" +related: + - "OpenAI" --- # Anthropic diff --git a/entities/ai-alignment/google-deepmind.md b/entities/ai-alignment/google-deepmind.md index dbf5eadd..6113d165 100644 --- a/entities/ai-alignment/google-deepmind.md +++ b/entities/ai-alignment/google-deepmind.md @@ -1,4 +1,6 @@ --- + + type: entity entity_type: lab name: "Google DeepMind" @@ -21,6 +23,12 @@ competitors: ["OpenAI", "Anthropic", "xAI"] tracked_by: theseus created: 2026-03-16 last_updated: 2026-03-16 +related: + - "OpenAI" + - "xAI" +reweave_edges: + - "OpenAI|related|2026-03-28" + - "xAI|related|2026-03-28" --- # Google DeepMind diff --git a/entities/ai-alignment/openai.md b/entities/ai-alignment/openai.md index c828d6aa..c68c7ee7 100644 --- a/entities/ai-alignment/openai.md +++ b/entities/ai-alignment/openai.md @@ -1,4 +1,10 @@ --- + + + + + + type: entity entity_type: lab name: "OpenAI" @@ -22,6 +28,21 @@ competitors: ["Anthropic", "Google DeepMind", "xAI"] tracked_by: theseus created: 2026-03-16 last_updated: 2026-03-16 +related: + - "Anthropic" + - "Dario Amodei" + - "Google DeepMind" + - "Safe Superintelligence Inc." + - "xAI" +reweave_edges: + - "Anthropic|related|2026-03-28" + - "Dario Amodei|related|2026-03-28" + - "Google DeepMind|related|2026-03-28" + - "Safe Superintelligence Inc.|related|2026-03-28" + - "Thinking Machines Lab|supports|2026-03-28" + - "xAI|related|2026-03-28" +supports: + - "Thinking Machines Lab" --- # OpenAI diff --git a/entities/ai-alignment/xai.md b/entities/ai-alignment/xai.md index 19a88913..8dc1f5a7 100644 --- a/entities/ai-alignment/xai.md +++ b/entities/ai-alignment/xai.md @@ -1,4 +1,6 @@ --- + + type: entity entity_type: lab name: "xAI" @@ -20,6 +22,12 @@ competitors: ["OpenAI", "Anthropic", "Google DeepMind"] tracked_by: theseus created: 2026-03-16 last_updated: 2026-03-16 +related: + - "Google DeepMind" + - "OpenAI" +reweave_edges: + - "Google DeepMind|related|2026-03-28" + - "OpenAI|related|2026-03-28" --- # xAI diff --git a/foundations/collective-intelligence/RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md b/foundations/collective-intelligence/RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md index 1465a0de..cf9769e0 100644 --- a/foundations/collective-intelligence/RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md +++ b/foundations/collective-intelligence/RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md @@ -1,10 +1,25 @@ --- + + + + description: The dominant alignment paradigms share a core limitation -- human preferences are diverse distributional and context-dependent not reducible to one reward function type: claim domain: collective-intelligence created: 2026-02-17 source: "DPO Survey 2025 (arXiv 2503.11701)" confidence: likely +related: + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training" + - "rlhf is implicit social choice without normative scrutiny" + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous" +reweave_edges: + - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training|related|2026-03-28" + - "rlhf is implicit social choice without normative scrutiny|related|2026-03-28" + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness|supports|2026-03-28" + - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed parameter behavior when preferences are homogeneous|related|2026-03-28" +supports: + - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness" --- # RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values diff --git a/foundations/collective-intelligence/collective intelligence requires diversity as a structural precondition not a moral preference.md b/foundations/collective-intelligence/collective intelligence requires diversity as a structural precondition not a moral preference.md index b95da753..4c789936 100644 --- a/foundations/collective-intelligence/collective intelligence requires diversity as a structural precondition not a moral preference.md +++ b/foundations/collective-intelligence/collective intelligence requires diversity as a structural precondition not a moral preference.md @@ -1,10 +1,15 @@ --- + description: Ashby's Law of Requisite Variety, Kauffman's adjacent possible, Page's diversity theorem, and Henrich's Tasmanian regression all prove diversity is a physical law of adaptive systems type: claim domain: collective-intelligence created: 2026-02-16 confidence: proven source: "TeleoHumanity Manifesto, Chapter 4" +supports: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions" +reweave_edges: + - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions|supports|2026-03-28" --- # collective intelligence requires diversity as a structural precondition not a moral preference diff --git a/foundations/collective-intelligence/principal-agent problems arise whenever one party acts on behalf of another with divergent interests and unobservable effort because information asymmetry makes perfect contracts impossible.md b/foundations/collective-intelligence/principal-agent problems arise whenever one party acts on behalf of another with divergent interests and unobservable effort because information asymmetry makes perfect contracts impossible.md index 387409b6..a09e5143 100644 --- a/foundations/collective-intelligence/principal-agent problems arise whenever one party acts on behalf of another with divergent interests and unobservable effort because information asymmetry makes perfect contracts impossible.md +++ b/foundations/collective-intelligence/principal-agent problems arise whenever one party acts on behalf of another with divergent interests and unobservable effort because information asymmetry makes perfect contracts impossible.md @@ -1,10 +1,15 @@ --- + type: claim domain: collective-intelligence description: "The formal basis for oversight problems: when agents have private information or unobservable actions, principals cannot design contracts that fully align incentives, creating irreducible gaps between intended and actual behavior" confidence: proven source: "Jensen & Meckling (1976); Akerlof, Market for Lemons (1970); Holmström (1979); Arrow (1963)" created: 2026-03-07 +related: + - "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" +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" --- # principal-agent problems arise whenever one party acts on behalf of another with divergent interests and unobservable effort because information asymmetry makes perfect contracts impossible 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 a6e3016d..06e83f7c 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 @@ -1,10 +1,18 @@ --- + + 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 created: 2026-02-17 source: "AI Safety Forum discussions; multiple alignment researchers 2025" 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" +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 alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it diff --git a/foundations/critical-systems/optimization for efficiency without regard for resilience creates systemic fragility because interconnected systems transmit and amplify local failures into cascading breakdowns.md b/foundations/critical-systems/optimization for efficiency without regard for resilience creates systemic fragility because interconnected systems transmit and amplify local failures into cascading breakdowns.md index 1bc38f46..f4d02245 100644 --- a/foundations/critical-systems/optimization for efficiency without regard for resilience creates systemic fragility because interconnected systems transmit and amplify local failures into cascading breakdowns.md +++ b/foundations/critical-systems/optimization for efficiency without regard for resilience creates systemic fragility because interconnected systems transmit and amplify local failures into cascading breakdowns.md @@ -1,4 +1,5 @@ --- + description: Globalized supply chains lean healthcare infrastructure and overleveraged financial systems all optimize for efficiency during normal times while accumulating hidden tail risk that materializes catastrophically during shocks type: claim domain: critical-systems @@ -6,6 +7,10 @@ source: "Architectural Investing, Ch. Introduction; Taleb (Black Swan)" confidence: proven tradition: "complexity economics, risk management, Teleological Investing" created: 2026-02-28 +related: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on" +reweave_edges: + - "delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on|related|2026-03-28" --- # optimization for efficiency without regard for resilience creates systemic fragility because interconnected systems transmit and amplify local failures into cascading breakdowns diff --git a/foundations/cultural-dynamics/collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution.md b/foundations/cultural-dynamics/collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution.md index 3f859991..69fad273 100644 --- a/foundations/cultural-dynamics/collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution.md +++ b/foundations/cultural-dynamics/collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution.md @@ -1,10 +1,15 @@ --- + type: claim domain: cultural-dynamics description: "Olson's logic of collective action: large groups systematically underprovide public goods because individual incentives favor free-riding, and this problem worsens with group size — small concentrated groups outorganize large diffuse ones" confidence: proven source: "Olson 1965 The Logic of Collective Action; Ostrom 1990 Governing the Commons (boundary condition)" created: 2026-03-08 +related: + - "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" +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" --- # collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution