reweave: connect 31 orphan claims via vector similarity

Threshold: 0.7, Haiku classification, 33 files modified.

Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>

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/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 +1,15 @@
 ---
+
 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
+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"
+reweave_edges:
+  - "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"
 ---
 
 # 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 alignment is a coordination problem not a technical problem.md

@@ -1,10 +1,21 @@
 ---
+
+
+
 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 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"
+  - "Anthropic"
+  - "Dario Amodei"
+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"
+  - "Anthropic|related|2026-03-28"
+  - "Dario Amodei|related|2026-03-28"
 ---
 
 # AI alignment is a coordination problem not a technical problem

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/deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices.md

@@ -1,10 +1,18 @@
 ---
+
+
 type: claim
 domain: ai-alignment
 description: "AI agents amplify existing expertise rather than replacing it because practitioners who understand what agents can and cannot do delegate more precisely, catch errors faster, and design better workflows"
 confidence: likely
 source: "Andrej Karpathy (@karpathy) and Simon Willison (@simonw), practitioner observations Feb-Mar 2026"
 created: 2026-03-09
+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"
+  - "the progression from autocomplete to autonomous agent teams follows a capability matched escalation where premature adoption creates more chaos than value"
+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"
+  - "the progression from autocomplete to autonomous agent teams follows a capability matched escalation where premature adoption creates more chaos than value|related|2026-03-28"
 ---
 
 # Deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices

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/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md

@@ -1,4 +1,5 @@
 ---
+
 type: claim
 domain: ai-alignment
 secondary_domains: [collective-intelligence, cultural-dynamics]
@@ -11,6 +12,10 @@ 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"
 ---
 
 # high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects

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 +1,6 @@
 ---
+
+
 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 +8,12 @@ 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"
+  - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness"
+reweave_edges:
+  - "minority preference alignment improves 33 percent without majority compromise suggesting single reward leaves value on table|supports|2026-03-28"
+  - "single reward rlhf cannot align diverse preferences because alignment gap grows proportional to minority distinctiveness|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/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/pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md

@@ -1,10 +1,15 @@
 ---
+
 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
+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"
 ---
 
 # pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state

domains/ai-alignment/representative-sampling-and-deliberative-mechanisms-should-replace-convenience-platforms-for-ai-alignment-feedback.md

@@ -1,4 +1,5 @@
 ---
+
 type: claim
 domain: ai-alignment
 secondary_domains: [mechanisms, collective-intelligence]
@@ -6,6 +7,10 @@ description: "AI alignment feedback should use citizens assemblies or representa
 confidence: likely
 source: "Conitzer et al. (2024), 'Social Choice Should Guide AI Alignment' (ICML 2024)"
 created: 2026-03-11
+supports:
+  - "rlhf is implicit social choice without normative scrutiny"
+reweave_edges:
+  - "rlhf is implicit social choice without normative scrutiny|supports|2026-03-28"
 ---
 
 # Representative sampling and deliberative mechanisms should replace convenience platforms for AI alignment feedback

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 +1,19 @@
 ---
+
+
 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
+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"
+  - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training|related|2026-03-28"
+related:
+  - "rlchf aggregated rankings variant combines evaluator rankings via social welfare function before reward model training"
 ---
 
 # 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 +1,18 @@
 ---
+
+
 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"
+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"
 ---
 
 # 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/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/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.md

@@ -1,10 +1,15 @@
 ---
+
 type: claim
 domain: health
 description: "AI-native healthcare companies generate $500K-1M+ ARR per FTE compared to $100-200K for traditional health services, compressing time-to-$100M-ARR from 10+ years to under 5, creating a structural unit economics advantage that incumbents cannot match without rebuilding"
 confidence: likely
 source: "Bessemer Venture Partners, State of Health AI 2026 (bvp.com/atlas/state-of-health-ai-2026)"
 created: 2026-03-07
+related:
+  - "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|related|2026-03-28"
 ---
 
 # 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

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/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 +1,18 @@
 ---
+
+
 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:
+  - "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"
+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

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 +1,15 @@
 ---
+
 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"
+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"
 ---
 
 # 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/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

@@ -1,4 +1,6 @@
 ---
+
+
 type: entity
 entity_type: lab
 name: "Anthropic"
@@ -25,6 +27,13 @@ competitors: ["OpenAI", "Google DeepMind", "xAI"]
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+supports:
+  - "Dario Amodei"
+reweave_edges:
+  - "Dario Amodei|supports|2026-03-28"
+  - "OpenAI|related|2026-03-28"
+related:
+  - "OpenAI"
 ---
 
 # Anthropic

entities/ai-alignment/dario-amodei.md

@@ -1,4 +1,5 @@
 ---
+
 type: entity
 entity_type: person
 name: "Dario Amodei"
@@ -16,6 +17,10 @@ known_positions:
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+supports:
+  - "Anthropic"
+reweave_edges:
+  - "Anthropic|supports|2026-03-28"
 ---
 
 # Dario Amodei

entities/ai-alignment/google-deepmind.md

@@ -1,4 +1,5 @@
 ---
+
 type: entity
 entity_type: lab
 name: "Google DeepMind"
@@ -21,6 +22,10 @@ competitors: ["OpenAI", "Anthropic", "xAI"]
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+related:
+  - "OpenAI"
+reweave_edges:
+  - "OpenAI|related|2026-03-28"
 ---
 
 # Google DeepMind

entities/ai-alignment/openai.md

@@ -1,4 +1,7 @@
 ---
+
+
+
 type: entity
 entity_type: lab
 name: "OpenAI"
@@ -22,6 +25,15 @@ competitors: ["Anthropic", "Google DeepMind", "xAI"]
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+related:
+  - "Anthropic"
+  - "Google DeepMind"
+reweave_edges:
+  - "Anthropic|related|2026-03-28"
+  - "Google DeepMind|related|2026-03-28"
+  - "Thinking Machines Lab|supports|2026-03-28"
+supports:
+  - "Thinking Machines Lab"
 ---
 
 # OpenAI

entities/ai-alignment/thinking-machines-lab.md

@@ -1,4 +1,5 @@
 ---
+
 type: entity
 entity_type: lab
 name: "Thinking Machines Lab"
@@ -20,6 +21,10 @@ competitors: ["OpenAI", "Anthropic", "SSI"]
 tracked_by: theseus
 created: 2026-03-16
 last_updated: 2026-03-16
+supports:
+  - "OpenAI"
+reweave_edges:
+  - "OpenAI|supports|2026-03-28"
 ---
 
 # Thinking Machines Lab

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 +1,19 @@
 ---
+
+
 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:
+  - "rlhf is implicit social choice without normative scrutiny"
+reweave_edges:
+  - "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"
+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