Merge pull request 'reweave: connect 48 orphan claims via vector similarity' (#2081) from reweave/2026-03-28 into main

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

entities/ai-alignment/anthropic.md

@@ -25,6 +25,12 @@ competitors: ["OpenAI", "Google DeepMind", "xAI"]
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+related:
+  - "Dario Amodei"
+  - "OpenAI"
+reweave_edges:
+  - "Dario Amodei|related|2026-03-28"
+  - "OpenAI|related|2026-03-28"
 ---
 
 # Anthropic

entities/ai-alignment/google-deepmind.md

@@ -21,6 +21,12 @@ 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

entities/ai-alignment/openai.md

@@ -22,6 +22,20 @@ 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."
+  - "Thinking Machines Lab"
+  - "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|related|2026-03-28"
+  - "xAI|related|2026-03-28"
 ---
 
 # OpenAI

entities/ai-alignment/xai.md

@@ -20,6 +20,12 @@ 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

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

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

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

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

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

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